Nonparametric Modeling of the Left Censorship of Analytical Data in Food Risk Assessment
食品风险评估中分析数据左删失的非参数建模AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要中的风险指食品毒素摄入超标概率，属于食品安全统计，不能据此归入金融尾部或系统性风险。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
食品风险评估中分析数据左删失的非参数建模
- 研究问题
- 当食品污染浓度低于检测或定量限、消费与污染数据分别获得时，如何估计摄入量超过暂定每周耐受摄入量的概率？
- 方法与思路
- 作者以 Kaplan–Meier 估计替代通常的经验分布函数，处理污染数据左删失，并推导暴露风险估计的渐近性质与双重自助置信区间。
- 材料陈述的贡献
- 摘要称，研究提供风险估计的详细算法；法国赭曲霉毒素 A 应用提示低龄儿童风险较高，特定食品限值未显著降低风险。
- 阅读关注点
- 应核对左删失处理所需条件、独立消费与污染资料的组合方式，以及双重自助法和食品限值情景计算中的不确定性。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要；删失条件、风险数值与限值设定未说明，相关结论限于摘要所述食品暴露应用。
原文摘要
Contaminants and natural toxicants such as mycotoxins may be present in various food items that may be considered dangerous to human health if the cumulative intake remains above the toxicologic safe references. This intake or exposure can be estimated using both consumption surveys and analytical data that record the contamination levels of food. Analytical data often present some left censorship, that is, data below some limit of detection or quantification. This article proposes the integration of a nonparametric modeling of the left censorship of analytical data in a model aiming at giving a quantitative evaluation of the risk due to the presence of some particular contaminants in food. We focus on the estimation of the “risk,” defined as the probability for exposure to exceed the so-called “provisional tolerable weekly intake” (PTWI), when both consumption data and contamination data are independently available. To account for the left censorship of the contamination data (due to the existence of detection/quantification limits), we propose using a Kaplan–Meier estimator instead of the empirical cumulative distribution function generally used in nonparametric procedures. We give the asymptotic behavior of our estimator and derive the asymptotic properties of the associated risk estimator. Several confidence intervals are obtained using a double-bootstrap procedure. A detailed algorithm is proposed. As an illustration, we present an evaluation of the risk exposure to ochratoxin A in France and use our risk estimator to show that children under age 10 years are a population at particular risk. Imposing some maximum limits on particular food items, namely cereals and wine, would not significantly reduce the risk.